Maxim Raginsky

181 papers A* 9A 2B 23C 7Misc 5Journal 104Unranked 31

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Majorizing Measures, Codes, and Information. IEEE Trans. Inf. Theory Yifeng Chu, Maxim Raginsky
2026	J	`jnl`	Spatially-Coupled Network RNA Velocities: A Control-Theoretic Perspective. CoRR Boya Hou, Maxim Raginsky, Abhishek Pandey, Olgica Milenkovic
2025	J	`jnl`	Expressivity of Quadratic Neural ODEs. CoRR Joshua Hanson, Maxim Raginsky
2025	J	`jnl`	Fitting an immersed submanifold to data via Sussmann's orbit theorem. Math. Control. Signals Syst. Joshua Hanson, Maxim Raginsky
2025	J	`jnl`	Generalization Bounds: Perspectives from Information Theory and PAC-Bayes. Found. Trends Mach. Learn. Fredrik Hellström, Giuseppe Durisi, Benjamin Guedj, Maxim Raginsky
2024	—	`conf`	A Variational Approach to Sampling in Diffusion Processes. CDC Maxim Raginsky
2024	J	`jnl`	Rademacher Complexity of Neural ODEs via Chen-Fliess Series. CoRR Joshua Hanson, Maxim Raginsky
2024	—	`conf`	Rademacher complexity of neural ODEs via Chen-Fliess series. L4DC Joshua Hanson, Maxim Raginsky
2024	J	`jnl`	Revisiting Stochastic Realization Theory using Functional Itô Calculus. CoRR Tanya Veeravalli, Maxim Raginsky
2024	J	`jnl`	Transformer-Based Models Are Not Yet Perfect At Learning to Emulate Structural Recursion. CoRR Shizhuo Dylan Zhang, Curt Tigges, Zory Zhang, Stella Biderman, Maxim Raginsky, Talia Ringer
2024	J	`jnl`	Transformer-Based Models Are Not Yet Perfect At Learning to Emulate Structural Recursion. Trans. Mach. Learn. Res. Shizhuo Dylan Zhang, Curt Tigges, Zory Zhang, Stella Biderman, Maxim Raginsky, Talia Ringer
2023	J	`jnl`	A Chain Rule for the Expected Suprema of Bernoulli Processes. CoRR Yifeng Chu, Maxim Raginsky
2023	—	`conf`	A Constructive Approach to Function Realization by Neural Stochastic Differential Equations. CDC Tanya Veeravalli, Maxim Raginsky
2023	J	`jnl`	A Constructive Approach to Function Realization by Neural Stochastic Differential Equations. CoRR Tanya Veeravalli, Maxim Raginsky
2023	A*	`conf`	A unified framework for information-theoretic generalization bounds. NeurIPS Yifeng Chu, Maxim Raginsky
2023	J	`jnl`	A unified framework for information-theoretic generalization bounds. CoRR Yifeng Chu, Maxim Raginsky
2023	J	`jnl`	Can Transformers Learn to Solve Problems Recursively? CoRR Shizhuo Dylan Zhang, Curt Tigges, Stella Biderman, Maxim Raginsky, Talia Ringer
2023	J	`jnl`	Generalization Bounds: Perspectives from Information Theory and PAC-Bayes. CoRR Fredrik Hellström, Giuseppe Durisi, Benjamin Guedj, Maxim Raginsky
2023	B	`conf`	Majorizing Measures, Codes, and Information. ISIT Yifeng Chu, Maxim Raginsky
2023	J	`jnl`	Majorizing Measures, Codes, and Information. CoRR Yifeng Chu, Maxim Raginsky
2023	—	`conf`	Nonlinear Controllability and Function Representation by Neural Stochastic Differential Equations. L4DC Tanya Veeravalli, Maxim Raginsky
2023	J	`jnl`	Partially Observed Discrete-Time Risk-Sensitive Mean Field Games. Dyn. Games Appl. Naci Saldi, Tamer Basar, Maxim Raginsky
2023	J	`jnl`	Variational Principles for Mirror Descent and Mirror Langevin Dynamics. CoRR Belinda Tzen, Anant Raj, Maxim Raginsky, Francis R. Bach
2023	J	`jnl`	Variational Principles for Mirror Descent and Mirror Langevin Dynamics. IEEE Control. Syst. Lett. Belinda Tzen, Anant Raj, Maxim Raginsky, Francis R. Bach
2022	A*	`ed.`	Conference on Learning Theory, 2-5 July 2022, London, UK. COLT Po-Ling Loh, Maxim Raginsky
2022	—	`conf`	Fitting an immersed submanifold to data via Sussmann's orbit theorem. CDC Joshua Hanson, Maxim Raginsky
2022	J	`jnl`	Fitting an immersed submanifold to data via Sussmann's orbit theorem. CoRR Joshua Hanson, Maxim Raginsky
2022	—	`conf`	Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits. L4DC Alan Yang, Jie Xiong, Maxim Raginsky, Elyse Rosenbaum
2022	J	`jnl`	Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits. CoRR Alan Yang, Jie Xiong, Maxim Raginsky, Elyse Rosenbaum
2022	J	`jnl`	Minimum Excess Risk in Bayesian Learning. IEEE Trans. Inf. Theory Aolin Xu, Maxim Raginsky
2022	J	`jnl`	Nonlinear controllability and function representation by neural stochastic differential equations. CoRR Tanya Veeravalli, Maxim Raginsky
2022	J	`jnl`	Robustness to incorrect models and data-driven learning in average-cost optimal stochastic control. Autom. Ali Devran Kara, Maxim Raginsky, Serdar Yüksel
2021	—	`conf`	EE-Grad: Exploration and Exploitation for Cost-Efficient Mini-Batch SGD. ACSCC Mehmet A. Donmez, Jeffrey Ludwig, Maxim Raginsky, Andrew C. Singer
2021	A*	`conf`	Information-theoretic generalization bounds for black-box learning algorithms. NeurIPS Hrayr Harutyunyan, Maxim Raginsky, Greg Ver Steeg, Aram Galstyan
2021	J	`jnl`	Information-theoretic generalization bounds for black-box learning algorithms. CoRR Hrayr Harutyunyan, Maxim Raginsky, Greg Ver Steeg, Aram Galstyan
2021	—	`conf`	Learning Recurrent Neural Net Models of Nonlinear Systems. L4DC Joshua Hanson, Maxim Raginsky, Eduardo D. Sontag
2021	—	`conf`	Neural Networks for Transient Modeling of Circuits : Invited Paper. MLCAD Jie Xiong, Alan Yang, Maxim Raginsky, Elyse Rosenbaum
2021	—	`conf`	Sampling, variational Bayesian inference, and conditioned stochastic differential equations. CDC Todd P. Coleman, Maxim Raginsky
2020	J	`jnl`	A mean-field theory of lazy training in two-layer neural nets: entropic regularization and controlled McKean-Vlasov dynamics. CoRR Belinda Tzen, Maxim Raginsky
2020	J	`jnl`	Approximate Markov-Nash Equilibria for Discrete-Time Risk-Sensitive Mean-Field Games. Math. Oper. Res. Naci Saldi, Tamer Basar, Maxim Raginsky
2020	J	`jnl`	Channel Polarization Through the Lens of Blackwell Measures. IEEE Trans. Inf. Theory Naveen Goela, Maxim Raginsky
2020	J	`jnl`	Learning Recurrent Neural Net Models of Nonlinear Systems. CoRR Joshua Hanson, Maxim Raginsky, Eduardo D. Sontag
2020	J	`jnl`	Minimum Excess Risk in Bayesian Learning. CoRR Aolin Xu, Maxim Raginsky
2020	A	`conf`	Model-Augmented Conditional Mutual Information Estimation for Feature Selection. UAI Alan Yang, AmirEmad Ghassami, Maxim Raginsky, Negar Kiyavash, Elyse Rosenbaum
2020	J	`jnl`	Partially Observed Discrete-Time Risk-Sensitive Mean Field Games. CoRR Naci Saldi, Tamer Basar, Maxim Raginsky
2020	—	`conf`	Universal Simulation of Stable Dynamical Systems by Recurrent Neural Nets. L4DC Joshua Hanson, Maxim Raginsky
2019	J	`jnl`	Approximate Nash Equilibria in Partially Observed Stochastic Games with Mean-Field Interactions. Math. Oper. Res. Naci Saldi, Tamer Basar, Maxim Raginsky
2019	J	`jnl`	Learning Finite-Dimensional Coding Schemes with Nonlinear Reconstruction Maps. SIAM J. Math. Data Sci. Jaeho Lee, Maxim Raginsky
2019	—	`conf`	Linear Noisy Networks with Stochastic Components. CDC Noyan C. Sevüktekin, Maxim Raginsky, Andrew C. Singer
2019	J	`jnl`	Model-Augmented Nearest-Neighbor Estimation of Conditional Mutual Information for Feature Selection. CoRR Alan Yang, AmirEmad Ghassami, Maxim Raginsky, Negar Kiyavash, Elyse Rosenbaum
2019	J	`jnl`	Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit. CoRR Belinda Tzen, Maxim Raginsky
2019	—	`conf`	Partially-Observed Discrete-Time Risk-Sensitive Mean-Field Games. CDC Naci Saldi, Tamer Basar, Maxim Raginsky
2019	—	`conf`	Robustness to Incorrect Models in Average-Cost Optimal Stochastic Control. CDC Ali Devran Kara, Maxim Raginsky, Serdar Yüksel
2019	A*	`conf`	Theoretical guarantees for sampling and inference in generative models with latent diffusions. COLT Belinda Tzen, Maxim Raginsky
2019	J	`jnl`	Theoretical guarantees for sampling and inference in generative models with latent diffusions. CoRR Belinda Tzen, Maxim Raginsky
2019	A*	`conf`	Universal Approximation of Input-Output Maps by Temporal Convolutional Nets. NeurIPS Joshua Hanson, Maxim Raginsky
2019	J	`jnl`	Universal Approximation of Input-Output Maps by Temporal Convolutional Nets. CoRR Joshua Hanson, Maxim Raginsky
2018	J	`jnl`	Channel Polarization through the Lens of Blackwell Measures. CoRR Naveen Goela, Maxim Raginsky
2018	J	`jnl`	Coordinate Dual Averaging for Decentralized Online Optimization With Nonseparable Global Objectives. IEEE Trans. Control. Netw. Syst. Soomin Lee, Angelia Nedic, Maxim Raginsky
2018	J	`jnl`	Discrete-time Risk-sensitive Mean-field Games. CoRR Naci Saldi, Tamer Basar, Maxim Raginsky
2018	J	`jnl`	Learning finite-dimensional coding schemes with nonlinear reconstruction maps. CoRR Jaeho Lee, Maxim Raginsky
2018	A*	`conf`	Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability. COLT Belinda Tzen, Tengyuan Liang, Maxim Raginsky
2018	J	`jnl`	Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability. CoRR Belinda Tzen, Tengyuan Liang, Maxim Raginsky
2018	J	`jnl`	Markov-Nash Equilibria in Mean-Field Games with Discounted Cost. SIAM J. Control. Optim. Naci Saldi, Tamer Basar, Maxim Raginsky
2018	A*	`conf`	Minimax Statistical Learning with Wasserstein distances. NeurIPS Jaeho Lee, Maxim Raginsky
2018	J	`jnl`	Sequential Empirical Coordination Under an Output Entropy Constraint. IEEE Trans. Inf. Theory Ehsan Shafieepoorfard, Maxim Raginsky
2018	B	`conf`	Sequential prediction with coded side information under logarithmic loss. ALT Yanina Shkel, Maxim Raginsky, Sergio Verdú
2018	—	`conf`	Stochastic modeling of air electrostatic discharge parameters. IRPS Yang Xiu, Samuel Sagan, Advika Battini, Xiao Ma, Maxim Raginsky, Elyse Rosenbaum
2018	B	`conf`	Universal Compression, List Decoding, and Logarithmic Loss. ISIT Yanina Shkel, Maxim Raginsky, Sergio Verdú
2017	J	`jnl`	Approximate Nash Equilibria in Partially Observed Stochastic Games with Mean-Field Interactions. CoRR Naci Saldi, Tamer Basar, Maxim Raginsky
2017	J	`jnl`	Cost-Performance Tradeoffs in Fusing Unreliable Computational Units. CoRR Mehmet A. Donmez, Maxim Raginsky, Andrew C. Singer, Lav R. Varshney
2017	J	`jnl`	EE-Grad: Exploration and Exploitation for Cost-Efficient Mini-Batch SGD. CoRR Mehmet A. Donmez, Maxim Raginsky, Andrew C. Singer
2017	J	`jnl`	Information-Theoretic Lower Bounds for Distributed Function Computation. IEEE Trans. Inf. Theory Aolin Xu, Maxim Raginsky
2017	J	`jnl`	Information-Theoretic Lower Bounds on Bayes Risk in Decentralized Estimation. IEEE Trans. Inf. Theory Aolin Xu, Maxim Raginsky
2017	—	`conf`	Information-theoretic analysis of generalization capability of learning algorithms. NIPS Aolin Xu, Maxim Raginsky
2017	J	`jnl`	Information-theoretic analysis of generalization capability of learning algorithms. CoRR Aolin Xu, Maxim Raginsky
2017	C	`conf`	Markov-Nash equilibria in mean-field games with discounted cost. ACC Naci Saldi, Tamer Basar, Maxim Raginsky
2017	J	`jnl`	Minimax Statistical Learning and Domain Adaptation with Wasserstein Distances. CoRR Jaeho Lee, Maxim Raginsky
2017	A*	`conf`	Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis. COLT Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky
2017	J	`jnl`	Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis. CoRR Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky
2017	Misc	`conf`	Rationally inattentive Markov decision processes over a finite horizon. ACSSC Ehsan Shafieepoorfard, Maxim Raginsky
2017	J	`jnl`	Sequential Empirical Coordination Under an Output Entropy Constraint. CoRR Ehsan Shafieepoorfard, Maxim Raginsky
2017	J	`jnl`	Stochastic Dual Averaging for Decentralized Online Optimization on Time-Varying Communication Graphs. IEEE Trans. Autom. Control. Soomin Lee, Angelia Nedic, Maxim Raginsky
2017	B	`conf`	Universal lossy compression under logarithmic loss. ISIT Yanina Shkel, Maxim Raginsky, Sergio Verdú
2016	—	`conf`	Active object detection on graphs via locally informative trees. MLSP Daphney-Stavroula Zois, Maxim Raginsky
2016	B	`conf`	Channel polarization and Blackwell measures. ISIT Maxim Raginsky
2016	J	`jnl`	Concentration of measure without independence: a unified approach via the martingale method. CoRR Aryeh Kontorovich, Maxim Raginsky
2016	Misc	`conf`	Cost-performance tradeoffs in unreliable computation architectures. ACSSC Mehmet A. Donmez, Maxim Raginsky, Andrew C. Singer, Lav R. Varshney
2016	J	`jnl`	Information-Theoretic Lower Bounds on Bayes Risk in Decentralized Estimation. CoRR Aolin Xu, Maxim Raginsky
2016	B	`conf`	Information-theoretic analysis of stability and bias of learning algorithms. ITW Maxim Raginsky, Alexander Rakhlin, Matthew Tsao, Yihong Wu, Aolin Xu
2016	J	`jnl`	Markov-Nash Equilibria in Mean-Field Games with Discounted Cost. CoRR Naci Saldi, Tamer Basar, Maxim Raginsky
2016	J	`jnl`	Online Discrete Optimization in Social Networks in the Presence of Knightian Uncertainty. Oper. Res. Maxim Raginsky, Angelia Nedic
2016	J	`jnl`	Online Optimization Under Adversarial Perturbations. IEEE J. Sel. Top. Signal Process. Mehmet A. Donmez, Maxim Raginsky, Andrew C. Singer
2016	J	`jnl`	Rationally Inattentive Control of Markov Processes. SIAM J. Control. Optim. Ehsan Shafieepoorfard, Maxim Raginsky, Sean P. Meyn
2016	—	`conf`	Regret minimization algorithms for single-controller zero-sum stochastic games. CDC Peng Guan, Maxim Raginsky, Rebecca Willett, Daphney-Stavroula Zois
2016	—	`conf`	Sequential empirical coordination under an output entropy constraint. CDC Ehsan Shafieepoorfard, Maxim Raginsky
2016	J	`jnl`	Strong Data Processing Inequalities and Φ-Sobolev Inequalities for Discrete Channels. IEEE Trans. Inf. Theory Maxim Raginsky
2015	J	`jnl`	Concentration of Measure Inequalities and Their Communication and Information-Theoretic Applications. CoRR Maxim Raginsky, Igal Sason
2015	B	`conf`	Converses for distributed estimation via strong data processing inequalities. ISIT Aolin Xu, Maxim Raginsky
2015	J	`jnl`	Converses for distributed estimation via strong data processing inequalities. CoRR Aolin Xu, Maxim Raginsky
2015	J	`jnl`	Decentralized Online Optimization with Global Objectives and Local Communication. CoRR Soomin Lee, Angelia Nedic, Maxim Raginsky
2015	C	`conf`	Decentralized online optimization with global objectives and local communication. ACC Angelia Nedic, Soomin Lee, Maxim Raginsky
2015	J	`jnl`	Information-theoretic lower bounds for distributed function computation. CoRR Aolin Xu, Maxim Raginsky
2015	B	`conf`	On MMSE estimation from quantized observations in the nonasymptotic regime. ISIT Jaeho Lee, Maxim Raginsky, Pierre Moulin
2015	J	`jnl`	On MMSE estimation from quantized observations in the nonasymptotic regime. CoRR Jaeho Lee, Maxim Raginsky, Pierre Moulin
2015	J	`jnl`	Poisson's Equation in Nonlinear Filtering. SIAM J. Control. Optim. Richard S. Laugesen, Prashant G. Mehta, Sean P. Meyn, Maxim Raginsky
2015	J	`jnl`	Rationally inattentive control of Markov processes. CoRR Ehsan Shafieepoorfard, Maxim Raginsky, Sean P. Meyn
2014	B	`conf`	A new information-theoretic lower bound for distributed function computation. ISIT Aolin Xu, Maxim Raginsky
2014	C	`conf`	From minimax value to low-regret algorithms for online Markov decision processes. ACC Peng Guan, Maxim Raginsky, Rebecca Willett
2014	J	`jnl`	Online Markov Decision Processes With Kullback-Leibler Control Cost. IEEE Trans. Autom. Control. Peng Guan, Maxim Raginsky, Rebecca M. Willett
2014	J	`jnl`	Online Markov decision processes with Kullback-Leibler control cost. CoRR Peng Guan, Maxim Raginsky, Rebecca Willett
2014	C	`conf`	Online discrete optimization in social networks. ACC Maxim Raginsky, Angelia Nedic
2014	—	`conf`	Poisson's equation in nonlinear filtering. CDC Richard S. Laugesen, Prashant G. Mehta, Sean P. Meyn, Maxim Raginsky
2014	J	`jnl`	Strong data processing inequalities and $Φ$-Sobolev inequalities for discrete channels. CoRR Maxim Raginsky
2013	J	`jnl`	Concentration of Measure Inequalities in Information Theory, Communications, and Coding. Found. Trends Commun. Inf. Theory Maxim Raginsky, Igal Sason
2013	J	`jnl`	Empirical Processes, Typical Sequences, and Coordinated Actions in Standard Borel Spaces. IEEE Trans. Inf. Theory Maxim Raginsky
2013	B	`conf`	Learning joint quantizers for reconstruction and prediction. ITW Maxim Raginsky
2013	B	`conf`	Logarithmic Sobolev inequalities and strong data processing theorems for discrete channels. ISIT Maxim Raginsky
2013	J	`jnl`	Online discrete optimization in social networks in the presence of Knightian uncertainty. CoRR Maxim Raginsky, Angelia Nedic
2013	C	`conf`	Rational inattention in controlled Markov processes. ACC Ehsan Shafieepoorfard, Maxim Raginsky, Sean P. Meyn
2013	—	`conf`	Rational inattention in scalar LQG control. CDC Ehsan Shafieepoorfard, Maxim Raginsky
2013	B	`conf`	Refined bounds on the empirical distribution of good channel codes via concentration inequalities. ISIT Maxim Raginsky, Igal Sason
2013	J	`jnl`	Relax but stay in control: from value to algorithms for online Markov decision processes. CoRR Peng Guan, Maxim Raginsky, Rebecca Willett
2012	J	`jnl`	Concentration of Measure Inequalities in Information Theory, Communications and Coding CoRR Maxim Raginsky, Igal Sason
2012	—	`conf`	Continuous-time stochastic Mirror Descent on a network: Variance reduction, consensus, convergence. CDC Maxim Raginsky, Jake V. Bouvrie
2012	C	`conf`	Online Markov decision processes with Kullback-Leibler control cost. ACC Peng Guan, Maxim Raginsky, Rebecca Willett
2012	J	`jnl`	Sequential Anomaly Detection in the Presence of Noise and Limited Feedback. IEEE Trans. Inf. Theory Maxim Raginsky, Rebecca M. Willett, Corinne Horn, Jorge G. Silva, Roummel F. Marcia
2012	J	`jnl`	Target detection performance bounds in compressive imaging. EURASIP J. Adv. Signal Process. Kalyani Krishnamurthy, Rebecca Willett, Maxim Raginsky
2011	C	`conf`	Decentralized Online Convex Programming with local information. ACC Maxim Raginsky, Nooshin Kiarashi, Rebecca Willett
2011	J	`jnl`	Directed information and Pearl's causal calculus CoRR Maxim Raginsky
2011	—	`conf`	Directed information and pearl's causal calculus. Allerton Maxim Raginsky
2011	J	`jnl`	Information-Based Complexity, Feedback and Dynamics in Convex Programming. IEEE Trans. Inf. Theory Maxim Raginsky, Alexander Rakhlin
2011	—	`conf`	Lower Bounds for Passive and Active Learning. NIPS Maxim Raginsky, Alexander Rakhlin
2011	J	`jnl`	Performance Bounds for Expander-Based Compressed Sensing in Poisson Noise. IEEE Trans. Signal Process. Maxim Raginsky, Sina Jafarpour, Zachary T. Harmany, Roummel F. Marcia, Rebecca M. Willett, A. Robert Calderbank
2011	B	`conf`	Shannon meets Blackwell and Le Cam: Channels, codes, and statistical experiments. ISIT Maxim Raginsky
2010	J	`jnl`	Compressed sensing performance bounds under Poisson noise. IEEE Trans. Signal Process. Maxim Raginsky, Rebecca Willett, Zachary T. Harmany, Roummel F. Marcia
2010	J	`jnl`	Divergence-based characterization of fundamental limitations of adaptive dynamical systems CoRR Maxim Raginsky
2010	—	`conf`	Divergence-based characterization of fundamental limitations of adaptive dynamical systems. Allerton Maxim Raginsky
2010	B	`conf`	Empirical processes and typical sequences. ISIT Maxim Raginsky
2010	J	`jnl`	Empirical processes, typical sequences and coordinated actions in standard Borel spaces CoRR Maxim Raginsky
2010	Misc	`conf`	Fishing in Poisson streams: Focusing on the whales, ignoring the minnows. CISS Maxim Raginsky, Sina Jafarpour, Rebecca Willett, A. Robert Calderbank
2010	J	`jnl`	Fishing in Poisson streams: focusing on the whales, ignoring the minnows CoRR Maxim Raginsky, Sina Jafarpour, Rebecca Willett, A. Robert Calderbank
2010	Misc	`conf`	Hyperspectral target detection from incoherent projections. ICASSP Kalyani Krishnamurthy, Maxim Raginsky, Rebecca Willett
2010	B	`conf`	Hyperspectral target detection from incoherent projections: Nonequiprobable targets and inhomogeneous SNR. ICIP Kalyani Krishnamurthy, Maxim Raginsky, Rebecca Willett
2010	J	`jnl`	Information-based complexity, feedback and dynamics in sequential convex programming CoRR Maxim Raginsky, Alexander Rakhlin
2010	J	`jnl`	Multiscale Photon-Limited Spectral Image Reconstruction. SIAM J. Imaging Sci. Kalyani Krishnamurthy, Maxim Raginsky, Rebecca Willett
2010	B	`conf`	Mutual information saddle points in channels of exponential family type. ISIT Todd P. Coleman, Maxim Raginsky
2010	—	`conf`	Online Convex Programming and regularization in adaptive control. CDC Maxim Raginsky, Alexander Rakhlin, Serdar Yüksel
2010	J	`jnl`	Performance bounds for expander-based compressed sensing in Poisson noise CoRR Maxim Raginsky, Sina Jafarpour, Zachary T. Harmany, Roummel F. Marcia, Rebecca Willett, A. Robert Calderbank
2009	J	`jnl`	A low-complexity universal scheme for rate-constrained distributed regression using a wireless sensor network. IEEE Trans. Signal Process. Avon L. Fernandes, Maxim Raginsky, Todd P. Coleman
2009	J	`jnl`	Achievability results for statistical learning under communication constraints CoRR Maxim Raginsky
2009	B	`conf`	Achievability results for statistical learning under communication constraints. ISIT Maxim Raginsky
2009	A*	`conf`	An empirical Bayes approach to contextual region classification. CVPR Svetlana Lazebnik, Maxim Raginsky
2009	J	`jnl`	Compressed sensing performance bounds under Poisson noise CoRR Maxim Raginsky, Zachary T. Harmany, Roummel F. Marcia, Rebecca Willett
2009	—	`conf`	Information complexity of black-box convex optimization: A new look via feedback information theory. Allerton Maxim Raginsky, Alexander Rakhlin
2009	J	`jnl`	Joint universal lossy coding and identification of stationary mixing sources with general alphabets CoRR Maxim Raginsky
2009	J	`jnl`	Joint universal lossy coding and identification of stationary mixing sources with general alphabets. IEEE Trans. Inf. Theory Maxim Raginsky
2009	—	`conf`	Locality-sensitive binary codes from shift-invariant kernels. NIPS Maxim Raginsky, Svetlana Lazebnik
2009	J	`jnl`	Minimax risk for Poisson compressed sensing CoRR Rebecca Willett, Maxim Raginsky
2009	J	`jnl`	Performance Bounds for Expander-based Compressed Sensing in the presence of Poisson Noise CoRR Sina Jafarpour, Rebecca Willett, Maxim Raginsky, A. Robert Calderbank
2009	B	`conf`	Performance bounds on compressed sensing with Poisson noise. ISIT Rebecca M. Willett, Maxim Raginsky
2009	J	`jnl`	Sequential anomaly detection in the presence of noise and limited feedback CoRR Maxim Raginsky, Roummel F. Marcia, Jorge G. Silva, Rebecca Willett
2009	B	`conf`	Sequential probability assignment via online convex programming using exponential families. ISIT Maxim Raginsky, Roummel F. Marcia, Jorge G. Silva, Rebecca M. Willett
2009	J	`jnl`	Supervised Learning of Quantizer Codebooks by Information Loss Minimization. IEEE Trans. Pattern Anal. Mach. Intell. Svetlana Lazebnik, Maxim Raginsky
2008	Misc	`conf`	A low-complexity universal scheme for rate-constrained distributed regression using a wireless sensor network. ICASSP Avon L. Fernandes, Maxim Raginsky, Todd P. Coleman
2008	J	`jnl`	Cooperation in self-organizing map networks enhances information transmission in the presence of input background activity. Biol. Cybern. Maxim Raginsky, Thomas J. Anastasio
2008	J	`jnl`	Joint Fixed-Rate Universal Lossy Coding and Identification of Continuous-Alphabet Memoryless Sources. IEEE Trans. Inf. Theory Maxim Raginsky
2008	—	`conf`	Near-minimax recursive density estimation on the binary hypercube. NIPS Maxim Raginsky, Svetlana Lazebnik, Rebecca Willett, Jorge G. Silva
2008	—	`conf`	On the information capacity of Gaussian channels under small peak power constraints. Allerton Maxim Raginsky
2008	B	`conf`	Universal Wyner-Ziv coding of discrete memoryless sources with known side information statistics. ISIT Maxim Raginsky
2007	B	`conf`	Joint Universal Lossy Coding and Identification of Stationary Mixing Sources. ISIT Maxim Raginsky
2007	J	`jnl`	Joint universal lossy coding and identification of stationary mixing sources CoRR Maxim Raginsky
2007	A	`conf`	Learning Nearest-Neighbor Quantizers from Labeled Data by Information Loss Minimization. AISTATS Svetlana Lazebnik, Maxim Raginsky
2007	J	`jnl`	Learning from compressed observations CoRR Maxim Raginsky
2006	B	`conf`	Joint Universal Lossy Coding and Identification of I.I.D. Vector Sources. ISIT Maxim Raginsky
2006	J	`jnl`	Joint universal lossy coding and identification of i.i.d. vector sources CoRR Maxim Raginsky
2005	J	`jnl`	A complexity-regularized quantization approach to nonlinear dimensionality reduction CoRR Maxim Raginsky
2005	B	`conf`	A complexity-regularized quantization approach to nonlinear dimensionality reduction. ISIT Maxim Raginsky
2005	—	`conf`	Estimation of Intrinsic Dimensionality Using High-Rate Vector Quantization. NIPS Maxim Raginsky, Svetlana Lazebnik
2005	J	`jnl`	Joint fixed-rate universal lossy coding and identification of continuous-alphabet memoryless sources CoRR Maxim Raginsky
2003	J	`jnl`	Scaling and Renormalization in Fault-Tolerant Quantum Computers. Quantum Inf. Process. Maxim Raginsky